Respiration monitor control method combined with sleeping posture analysis

Abstract

The invention discloses a breathing monitor control method combined with sleeping posture analysis, and relates to the technical field of breathing monitoring equipment, a cascade sensor is introduced, sleeping posture variables are determined by collecting the state of a target user, breathing monitoring records are interacted, and the relative relation of sleeping posture-monitoring control conditions is mined; a self-adaptive control module is developed in a control system of the respiration monitor, a sleeping posture variable is taken as a regulation and control target, an adjustment decision based on the self-adaptive control module is executed, condition signal collection of a target user is triggered, a respiration signal is determined, the respiration signal is transmitted back, histogram segmentation and counting are executed, and the sleep posture variable is adjusted and controlled. And determining a breathing state and carrying out feedback response control on the breathing monitor. The method and the device are used for solving the technical problems of how to introduce sleeping posture analysis in respiration monitoring and how to realize accurate self-adaptive control in the prior art. Self-adaptive respiration monitoring regulation and control based on user postures are achieved, and the monitoring fitness and accuracy can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of respiratory monitoring equipment, and in particular to a respiratory monitor control method combined with sleeping posture analysis. Background Art

[0002] At present, traditional respiratory monitoring technology mainly focuses on the analysis of respiratory signals, such as chest and abdominal rise and fall, airflow changes, etc., and often ignores the impact of sleeping posture on respiratory status. During sleep, changes in the user's sleeping posture may have a direct impact on the smoothness of breathing, respiratory rate and ventilation volume. Especially when suffering from sleep apnea, snoring and other problems, sleeping posture is crucial to the regulation of respiratory status.

[0003] Existing respiratory monitoring technologies usually collect data through a single signal channel, which is limited to physiological signals such as chest and abdominal fluctuations or airflow changes, and cannot accurately identify and analyze the dynamic relationship between sleeping posture and respiratory status. Such traditional methods have certain limitations, especially when faced with complex changes in sleeping posture and corresponding respiratory changes, and often cannot adjust the monitoring strategy in real time, making it difficult to provide personalized health management solutions.

[0004] At present, although there are some advanced multi-parameter monitoring technologies, they often lack effective integration and collaborative processing mechanisms, and are unable to effectively combine the monitoring information of sleeping posture changes and breathing status, resulting in the inability to comprehensively and accurately assess the user's sleep quality and health status.

[0005] Therefore, how to introduce sleeping posture analysis into respiratory monitoring and achieve precise adaptive control has become a key challenge in the current technological development. Summary of the invention

[0006] The present application provides a respiratory monitor control method combined with sleep posture analysis, which is used to solve the technical problem of how to introduce sleep posture analysis into respiratory monitoring and achieve accurate adaptive control in the prior art.

[0007] In view of the above problems, the present application provides a respiratory monitor control method combined with sleeping posture analysis.

[0008] The present application provides a method for controlling a respiratory monitor combined with sleep posture analysis, the method comprising: introducing a cascade sensor, determining a sleep posture variable by collecting the state of a target user and coupling the inter-frame difference of posture point cloud data and cascade sensor data, wherein the cascade sensor is used to detect pressure data of various parts of the human body; interactive respiratory monitoring records and mines the relative relationship between sleep posture and monitoring control conditions, and develops an adaptive control module in the control system of the respiratory monitor, wherein the monitoring control conditions are determined by dividing the signal channels of the respiratory monitor and grading the monitoring parameters; taking the sleep posture variable as the control target, executing an adjustment decision based on the adaptive control module, triggering conditional signal collection of the target user, and determining a respiratory signal; transmitting the respiratory signal back and performing histogram segmentation and counting, determining the respiratory state and performing feedback response control on the respiratory monitor.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The embodiment of the present application provides a control method for a respiratory monitor combined with sleep posture analysis, which introduces a cascade sensor, determines the sleep posture variable by collecting the state of the target user and coupling the inter-frame difference of the posture point cloud data and the cascade sensor data, wherein the cascade sensor is used to detect the pressure data of various parts of the human body; interactive respiratory monitoring records and mines the relative relationship between the sleep posture and the monitoring control conditions, and develops an adaptive control module in the control system of the respiratory monitor, wherein the monitoring control conditions are determined by dividing the signal channel of the respiratory monitor and grading the monitoring parameter frequency; taking the sleep posture variable as the control target, executing the adjustment decision based on the adaptive control module, triggering the conditional signal collection of the target user, and determining the respiratory signal; returning the respiratory signal and performing histogram segmentation and counting, determining the respiratory state and performing feedback response control on the respiratory monitor. It is used to solve the technical problem of how to introduce sleep posture analysis in respiratory monitoring and realize accurate adaptive control in the prior art. Adaptive respiratory monitoring and control based on user posture is realized, which can effectively improve the adaptability and accuracy of monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 A schematic flow chart of a respiratory monitor control method combined with sleeping posture analysis is provided for this application;

[0012] Figure 2 The present application provides a schematic diagram of a relative relationship mining process between sleeping posture and monitoring control conditions in a respiratory monitor control method combined with sleeping posture analysis. DETAILED DESCRIPTION

[0013] This application provides a method for controlling a respiratory monitor combined with sleep posture analysis, introduces a cascade sensor, determines the sleep posture variable by collecting the state of the target user, records the interactive respiratory monitoring and mines the relative relationship between the sleep posture and the monitoring control conditions, develops an adaptive control module in the control system of the respiratory monitor, takes the sleep posture variable as the control target, executes the adjustment decision based on the adaptive control module, triggers the conditional signal collection of the target user, determines the respiratory signal, transmits the respiratory signal back and performs histogram segmentation and counting, determines the respiratory state and performs feedback response control on the respiratory monitor. It is used to solve the technical problem of how to introduce sleep posture analysis in respiratory monitoring and realize accurate adaptive control in the prior art.

[0014] Example: Figure 1 As shown, the present application provides a respiratory monitor control method combined with sleeping posture analysis, the method comprising:

[0015] S1: Introduce a cascade sensor to determine the sleeping posture variable by collecting the state of the target user and coupling the inter-frame difference of the posture point cloud data and the cascade sensor data, wherein the cascade sensor is used to detect the pressure data of various parts of the human body.

[0016] In the embodiment of the present application, the cascade sensor is a structure in which multiple sensor nodes are connected in series to detect pressure data at various parts of the human body. The sensor can cover different parts of the human body and collect pressure distribution information including the back, shoulders, waist, etc. These pressure data are helpful in judging the user's sleeping posture and the potential impact of pressure changes.

[0017] Next, the state of the target user is collected. In this embodiment, the state of the target user not only refers to the user's physiological data, but also includes the changes in his posture at different time periods during sleep. By collecting pressure data in real time through cascade sensors, the system can obtain detailed information about the user's sleeping posture, such as the contact area between the back and the bed surface, the pressure distribution between the shoulders and the pillow, etc., as the collected user state. This provides a basis for further analyzing the relationship between sleeping posture and breathing status.

[0018] The posture point cloud data is a three-dimensional data set representing the user's posture obtained through three-dimensional scanning technology, reflecting the relative position and angle changes of various parts of the user during sleep. The pressure data provided by the cascade sensor is combined with these posture data to form a multi-dimensional data model.

[0019] By coupling the posture point cloud data with the user status, the user's posture and force distribution can be measured to ensure the completeness of the posture data.

[0020] Further, by comparing two consecutive data sets, the difference in data can be measured to detect changes in user posture. The inter-frame difference technology can effectively distinguish dynamic changes in sleeping posture, such as the transition from supine to side or prone. The force distribution may also be different in the same posture, thereby revealing the specific variables of sleeping posture.

[0021] By calculating the inter-frame difference of the above data, the system can accurately determine the user's sleeping posture variables at each time point. These variables may include information such as the user's torso angle, the position relationship of the limbs, and the pressure contact with the bed surface. Sleeping posture variables provide an important adjustment basis for the respiratory monitor, and can adjust the respiratory monitoring parameters according to different sleeping postures, making respiratory monitoring more accurate and personalized.

[0022] In summary, by introducing cascade sensors and posture point cloud data, combined with inter-frame difference technology, it is possible to accurately capture and analyze the user's sleeping posture changes, thereby providing a more accurate basis for respiratory monitoring, achieving the purpose of optimizing monitoring effects and improving health management levels.

[0023] Furthermore, the step S1 of determining the sleeping posture variable in the present application includes:

[0024] Establish a communication connection between the cascade sensor and the respiratory monitor; introduce a synchronization timestamp constraint, the respiratory monitor performs sleeping posture radar detection of the target user, synchronously triggers the sensor data feedback of the cascade sensor, and determines the real-time posture point cloud and the real-time cascade sensor data; perform inter-frame differential coupling on the real-time posture point cloud and the real-time cascade sensor data to determine the sleeping posture variable.

[0025] In the present embodiment, a communication connection between the cascade sensor and the respiratory monitor is first established. This step is intended to ensure smooth data transmission between the cascade sensor and the respiratory monitor. The cascade sensor monitors the user's pressure data through multiple sensor nodes, and the respiratory monitor is responsible for collecting and processing physiological data related to the user's breathing. In order to achieve data intercommunication, the cascade sensor is connected to the respiratory monitor via a wireless communication protocol (such as Wi-Fi, Bluetooth, etc.), so that the real-time collected data can be seamlessly transmitted to the respiratory monitor for further processing.

[0026] Subsequently, the synchronization timestamp constraint is introduced. The synchronization timestamp ensures the temporal consistency of the data collected by the cascade sensor and the respiratory monitor. By attaching a timestamp to each data collection point, the system can accurately identify the moment of data collection, thereby ensuring that the data of each sensor can be compared and analyzed in the same time dimension. This constraint mechanism ensures that the system can accurately align data from different sources when performing multi-data source analysis, providing effective support for subsequent analysis.

[0027] Next, the respiratory monitor performs radar detection of the target user's sleeping posture. Specifically, radar detection of sleeping posture is to scan the target user's sleeping posture through radar or other sensing technologies (such as infrared sensing, ultrasonic measurement, etc.) to obtain the user's body posture change data in real time.

[0028] During this process, the sensor data of the cascade sensor is synchronously triggered to be transmitted back. Through timestamp constraints, the collection actions of the respiratory monitor and the cascade sensor are synchronized to ensure that the data collected by the two are strictly consistent in time. This synchronous triggering mechanism can ensure that when the respiratory monitor performs sleeping posture radar detection, the cascade sensor is simultaneously collecting the corresponding pressure data. These pressure data reflect the contact between various parts of the user's body and the bed surface, and can provide necessary information for further analysis of the user's sleeping posture.

[0029] Furthermore, by synchronously processing the data from the sleeping posture radar detection and the data sent back by the cascade sensor, the system can obtain the user's real-time posture point cloud (three-dimensional data representing the posture) and real-time cascade sensor data (data recording the changes in pressure in various parts).

[0030] Furthermore, the real-time posture point cloud and the real-time cascade sensor data are coupled by frame differential. This step uses the frame differential technology to compare two consecutive frames of data to detect changes in the user's posture and corresponding pressure changes. For example, for the posture point cloud, by comparing the posture point cloud data of the current frame with the previous frame, the user's body position changes (such as from supine to side-lying, etc.) can be identified. At the same time, for the cascade sensor data, by comparing the pressure distribution difference between the current frame and the previous frame, the frame differential can be used to capture small pressure changes, thereby identifying potential changes in the user's breathing pattern caused by posture changes during sleep.

[0031] Finally, through the results of inter-frame differential coupling, that is, the changes in posture point cloud and force distribution, the user's sleeping posture variables can be accurately calculated. These variables may include information such as the user's body posture angle, posture change rate, and pressure values ​​of various parts. These sleeping posture variables will serve as the basis for subsequent respiratory monitoring adjustments to ensure that the respiratory monitor can provide the best monitoring solution under different sleeping postures.

[0032] In summary, this embodiment can effectively capture and analyze changes in the user's sleeping posture through precise data synchronization, real-time monitoring, and inter-frame differential coupling technology, and provide data support for the precise adjustment of the respiratory monitor, thereby further optimizing the effect of respiratory monitoring.

[0033] Furthermore, the real-time posture point cloud and the real-time cascade sensor data are coupled by frame differential to determine the sleeping posture variable. Step S1 of the present application includes:

[0034] Taking the upper-level posture point cloud as the background frame and the real-time posture point cloud as the state frame, frame difference calculation is performed to determine the first posture variable; taking the upper-level cascade sensor data as the background frame and the real-time cascade sensor data as the state frame, frame difference calculation is performed to determine the second cascade sensor variable; coupling the first posture variable and the second cascade sensor variable based on relative posture to determine the sleeping posture variable.

[0035] In an embodiment of the present application, the upper posture point cloud is used as the background frame, and the real-time posture point cloud is used as the state frame, and inter-frame difference calculation is performed to determine the first posture variable. Specifically, the posture point cloud is obtained through sensors or radar technology, reflecting the three-dimensional coordinates and postures of various parts of the user. In this step, the background frame refers to the posture point cloud data at the previous moment, that is, the upper posture point cloud. The state frame refers to the posture point cloud data at the current moment. By comparing these two point cloud data, the system can calculate the change in the user's posture between two time points. This change is the first posture variable, which may include changes in the trunk angle, rotation or bending of the limb position, etc. Inter-frame difference calculation helps to capture these subtle posture changes and provide a basis for further analysis of the user's sleeping posture.

[0036] Next, the cascade sensor monitors the pressure data of various parts of the user through multiple nodes. In this step, the background frame refers to the pressure sensing data of the previous moment, and the status frame refers to the sensing data of the current moment. By performing a differential calculation on these two data, that is, comparing the previous frame rate with the current frame rate according to the pressure distribution position, measuring the difference, the change in the user's body pressure distribution is determined, and then the second cascade sensing variable is obtained. This variable may reflect the pressure changes in different parts (such as back, shoulders, waist, etc.) between two moments, thereby revealing the impact of changes in sleeping posture on body pressure distribution.

[0037] Then, the first posture variable is combined with the second cascade sensor variable, and the sleeping posture variable is further determined through the relative posture coupling method. Relative posture coupling refers to the analysis of posture changes combined with corresponding pressure changes, taking into account the impact of different postures on different parts of the body. For example, when the user changes from supine to side-lying, the pressure distribution on the back will change, and the angle of the torso will also change. By combining the information of these two, the system can accurately calculate the user's sleeping posture variables, which may include the angle of the torso, the pressure distribution of various parts of the body, the rate of posture change, etc.

[0038] In summary, by performing inter-frame difference calculation on the posture point cloud and cascade sensor data and coupling them based on relative posture, the user's sleeping posture changes can be accurately analyzed, providing an important basis for subsequent respiratory monitoring and adjustment, thereby achieving more accurate personalized health management.

[0039] S2: Interactive respiratory monitoring records and mines the relative relationship between sleeping posture and monitoring control conditions, and develops an adaptive control module in the control system of the respiratory monitor, wherein the monitoring control conditions are determined by dividing the signal channels of the respiratory monitor and grading the monitoring parameter frequencies.

[0040] In the embodiment of the present application, the respiratory monitoring record refers to the real-time collection and recording of the user's respiratory data by a respiratory monitor. These data include physiological parameters such as respiratory rate, respiratory waveform, and ventilation volume. By interactively comparing and analyzing the sleeping position data of the user, the system can dig out the relative relationship between sleeping position and respiratory monitoring conditions. For example, the system can find that certain sleeping positions (such as lying on your back) may cause changes in respiratory rate and breathing mode (such as chest and abdominal breathing), then the monitoring requirements corresponding to the respiratory rate and breathing mode are the monitoring and control conditions for the current sleeping position, and other sleeping positions (such as lying on your side) may have different effects on the respiratory waveform. Digging out these relative relationships provides an important basis for subsequent control module adjustments.

[0041] Next, an adaptive control module is developed in the control system of the respiratory monitor. The main function of the adaptive control module is to automatically adjust the operating parameters of the system according to the real-time monitoring data to meet the needs of different users. In this embodiment, the adaptive control module can dynamically adjust the working mode of the respiratory monitor according to the user's sleeping position changes and breathing state. For example, when the system detects that the user changes his sleeping position, the control module can automatically adjust the collection type and frequency of the respiratory signal to ensure the accuracy and adaptability of the monitoring.

[0042] In this application, signal channel division refers to dividing the collected signal of the respiratory monitor into multiple channels so as to process different types of physiological signals separately. For example, the respiratory signal can be divided into a chest and abdomen fluctuation signal channel and an airflow breathing signal channel, and can also include a respiratory sound signal, etc. Among them, the chest and abdomen fluctuation signal channel is used to monitor the movement changes of the chest and abdomen, and the airflow breathing signal channel is used to monitor ventilation flow, temperature changes, etc. Through this division, the system can more accurately collect various types of data related to breathing.

[0043] Monitoring frequency classification refers to the hierarchical management of the monitoring frequency of signals according to different physiological signal characteristics. For example, for chest and abdominal fluctuation signals, the system may choose a lower sampling frequency, while for airflow change monitoring, a higher frequency may be required to capture subtle fluctuations. By adjusting the monitoring frequency, the system can optimize the accuracy and efficiency of data collection and reduce the collection of redundant data.

[0044] By dividing the signal channels and grading the monitoring frequency, the system can determine the monitoring control conditions, that is, the corresponding signal channels and monitoring frequency levels under different sleeping positions, and adjust the operation of the respiratory monitor according to these conditions. The determination of the control conditions can ensure that the monitor can always accurately obtain respiratory data under different sleeping positions, and perform appropriate processing and feedback.

[0045] In summary, this embodiment develops an adaptive control module by mining the relationship between sleeping posture and monitoring conditions, dividing signal channels and grading monitoring frequencies, which can dynamically adjust the parameters of the respiratory monitor to ensure accurate monitoring services under different sleeping postures and breathing conditions. This process greatly improves the intelligence and personalization capabilities of the monitoring system.

[0046] Further, such as Figure 2 As shown, the relative relationship between the digging sleeping posture and the monitoring control conditions, step S2 of the present application includes:

[0047] Divide the signal channels, wherein the signal channels at least include a first signal channel based on chest and abdominal breathing, and a second signal channel based on airflow breathing, the signal elements of the first signal channel at least include chest and abdominal rise and fall, and the signal elements of the second signal channel at least include temperature change, respiratory rate, and ventilation volume; divide the multi-level monitoring frequency parameters; determine the monitoring and control conditions based on the signal channel-multi-level monitoring frequency parameters, and combine the respiratory monitoring records to explore the relative relationship based on sleeping posture-monitoring and control conditions.

[0048] In this embodiment, the respiratory monitor is optimized by dividing the signal channels and grading the monitoring frequencies in combination with the relative relationship between the sleeping posture and the monitoring control conditions, so as to achieve more accurate respiratory monitoring.

[0049] First, the signal channels are divided, wherein the signal channels at least include a first signal channel based on chest and abdominal breathing, and a second signal channel based on airflow breathing. The signal channels are divided to enable the monitor to process different types of physiological signals separately. In the present embodiment, the first signal channel is mainly used to monitor the respiratory activity of the chest and abdomen, especially the ups and downs of the chest and abdomen during breathing. These changes reflect the user's chest and abdominal breathing, can reflect the user's breathing rhythm and depth, and help monitor the stability and depth of their breathing.

[0050] The second signal channel is used to monitor signals related to airflow, including temperature changes, respiratory rate, and ventilation volume. These elements can respectively reflect the temperature fluctuations of the airflow, the number of breaths per minute of the user, and the changes in airflow volume during each breath. These signals help capture the fluctuations of the airflow and analyze the breathing rate and air flow volume, thereby providing more comprehensive physiological information for the monitoring system.

[0051] Next, multiple levels of monitoring reference frequencies are set, that is, different monitoring frequencies are set for different types of signals. Since the chest and abdominal fluctuations change slowly, the system can set a lower sampling frequency, while airflow signals (such as temperature, respiratory rate, and ventilation volume) may contain faster fluctuations, so a higher sampling frequency is required to capture these subtle changes. Through this hierarchical sampling, the system can optimize data collection accuracy and processing efficiency and avoid the generation of redundant data.

[0052] Then, the monitoring control conditions are determined by the signal channel-multi-level monitoring reference frequency, that is, based on the aforementioned division of the signal channel and the setting of the sampling frequency, the system can specify a suitable reference frequency for each signal channel as a monitoring control condition. Through this control condition, the system can ensure effective monitoring of various types of signals in different sleeping positions while avoiding unnecessary errors. For example, when the user is in a supine position, the changes in the chest and abdomen are small, and the system can reduce the monitoring frequency of the chest and abdomen signal channels, while for airflow signals, a higher monitoring frequency still needs to be maintained.

[0053] Finally, combined with the respiratory monitoring records, the relative relationship between sleeping position and monitoring control conditions is mined. By analyzing the respiratory monitoring data of users in different sleeping positions, the relative relationship between sleeping position and monitoring control conditions can be mined. For example, certain sleeping positions may cause the amplitude of the chest and abdominal fluctuation signal to increase, while the airflow signal may show different fluctuation characteristics. According to the type of signal that needs to be collected in this sleeping position, the corresponding signal channel and frequency are configured. By mining this relative relationship, the system can better adjust the monitoring conditions to adapt to different changes in sleeping positions and improve monitoring accuracy.

[0054] In summary, this embodiment ensures that the respiratory monitoring system can accurately and effectively monitor physiological signals under different sleeping positions through the division of signal channels, the setting of multi-level monitoring frequency parameters, and the mining of the relative relationship between sleeping position and monitoring control conditions, thereby providing reliable support for personalized health management and intervention.

[0055] Furthermore, the signal channel division step S2 of the present application includes:

[0056] A third signal channel is introduced, wherein the third signal channel is an auxiliary signal channel, and the third signal source at least includes an electrocardiogram signal and a breathing sound signal, and is used to collect any third signal source; the control system is initialized and configured with the first signal channel and the second signal channel as the main ones and the third signal channel as the auxiliary ones, wherein single-channel collection, multi-channel same-frequency collection, and multi-channel difference-frequency collection are used as the collection methods.

[0057] This embodiment further optimizes the signal acquisition and processing process of the respiratory monitor, and enhances the multi-dimensional monitoring capability of the system by introducing a third signal channel and adjusting the acquisition method.

[0058] First, a third signal channel is introduced, wherein the third signal channel is an auxiliary signal channel. The introduction of the third signal channel is to supplement the monitoring range that the first signal channel (chest and abdominal respiratory signal) and the second signal channel (airflow respiratory signal) fail to cover. The function of this channel is to obtain other physiological signals related to respiratory health. Through the collection of these signals, the respiratory state and health status can be fully reflected. The third signal source includes at least an electrocardiogram signal and a respiratory sound signal. The electrocardiogram signal can provide cardiac activity data related to the respiratory system, especially when monitoring the interaction between breathing and cardiac health. The electrocardiogram signal is of great significance; the respiratory sound signal can capture the sounds made during the breathing process (such as snoring or the sound of difficulty breathing), which is crucial for identifying possible respiratory disorders (such as sleep apnea).

[0059] Next, the control system is initialized and configured with the first signal channel and the second signal channel as the main ones and the third signal channel as the auxiliary ones. In this embodiment, the first signal channel and the second signal channel are mainly responsible for the core respiratory monitoring task, and these two channels collect data from two dimensions: chest and abdominal fluctuations and airflow changes. The third signal channel is used as an auxiliary signal channel to supplement and enrich the monitoring content. When the system is initialized, through the configuration of the first, second and third signal channels, the control system can comprehensively consider the importance and collection priority of various signals, ensuring that the system fully utilizes the advantages of each channel when collecting data to achieve the best monitoring effect.

[0060] Then, single-channel acquisition, multi-channel same-frequency acquisition, and multi-channel difference frequency acquisition are used as acquisition methods. This step explains how to acquire and process signals through different acquisition methods. In the single-channel acquisition mode, the system only collects data from one signal channel (such as the chest and abdomen fluctuation signal channel). This mode is suitable for monitoring a certain type of signal or specific physiological indicators. In the multi-channel same-frequency acquisition mode, the system simultaneously collects data from multiple signal channels (such as the chest and abdomen fluctuation channel and the airflow signal channel) at the same frequency. This mode can synchronously monitor multiple physiological signals, thereby improving the real-time and comprehensiveness of the data. The multi-channel difference frequency acquisition mode uses different sampling frequencies between different signal channels for data acquisition. This mode can reduce unnecessary data redundancy while optimizing the timing of the signal, and dynamically adjust the acquisition frequency according to the characteristics of the signal to improve the acquisition efficiency.

[0061] In summary, through this configuration, the system can more comprehensively and accurately capture various signals related to the user's respiratory health, thereby providing more precise data support for health assessment and intervention.

[0062] S3: Taking the sleeping posture variable as the control target, executing the adjustment decision based on the adaptive control module, triggering the conditional signal collection of the target user, and determining the breathing signal.

[0063] First, the sleeping position variable is used as the control target. The sleeping position variable refers to the parameters or characteristic data obtained from the sleeping position analysis, which reflects the changes in the sleeping position state of the target user. Since different sleeping positions may have different effects on breathing, using the sleeping position variable as the control target can enable the system to dynamically adjust the monitoring strategy according to the user's actual sleeping position state, thereby improving the accuracy and sensitivity of monitoring.

[0064] Next, the adjustment decision based on the adaptive control module is executed. The adaptive control module can automatically optimize the control parameters according to the sleeping position variables of the target user and other monitoring data. The execution process of the adjustment decision includes many aspects, such as selecting a suitable signal acquisition channel, setting the sampling frequency, adjusting the data processing strategy, etc. This decision-making process not only takes into account the user's sleeping position, but also integrates other physiological signals (such as chest and abdominal rise and fall, airflow changes, etc.) to ensure that the system can continue to provide high-quality monitoring under changing environments and conditions.

[0065] Then, the conditional signal collection of the target user is triggered. Once the optimal monitoring conditions are determined based on the sleeping position variables and the adjustment decisions of the adaptive control module, the system will start signal collection. At this time, the system will trigger the corresponding sensor or signal channel for data collection based on the sleeping position of the target user, the required signal channel, and the adjusted sampling frequency. For example, if the user is in a supine position, the system may prioritize the collection of chest and abdominal rise and fall signals; if the user is in a side-lying position, it may focus more on collecting airflow change signals.

[0066] Finally, determine the breathing signal. By collecting the conditional signal, the system will process and analyze the raw data returned by the sensor, and finally determine the breathing signal of the target user. The breathing signal usually includes information such as chest and abdominal rise and fall, airflow changes, and breathing frequency. These data can reflect the user's breathing pattern and health status. The determination of the breathing signal not only provides basic data for subsequent health assessments, but also provides real-time feedback for respiratory intervention and optimization control.

[0067] In summary, this embodiment uses the adaptive control module adjustment decision based on the sleeping position variable, combined with the sleeping position status and other relevant signal data of the target user, to trigger appropriate signal acquisition, thereby accurately capturing and determining the user's breathing signal. Through this dynamic adjustment and precise acquisition, the system can provide more personalized and accurate breathing monitoring, ensuring that the health information of the target user can be effectively captured in various environments.

[0068] Further, the adjustment decision based on the adaptive control module is executed, and step S3 of the present application includes:

[0069] Identify the sleeping posture variable, determine a first dynamic variable based on the background frame, and a second dynamic variable based on the variable value; match the baseline control condition based on the first dynamic variable and the relative relationship; determine the conditional variable based on the second dynamic variable; adjust the baseline control condition with the conditional variable to determine the target control condition.

[0070] This embodiment involves optimizing the control conditions of a respiratory monitor through identification and adjustment of dynamic variables, thereby improving the accuracy and response speed of monitoring.

[0071] First, the sleeping posture variable is identified, and a first dynamic variable based on the background frame and a second dynamic variable based on the variable value are determined. In this step, the sleeping posture variable refers to a numerical value or parameter obtained according to the sleeping posture state of the target user, such as posture angle, position change, etc. Through dynamic analysis of these sleeping posture variables, two types of key dynamic variables can be identified: the user's sleeping posture state of the previous frame rate is used as the background frame. Since the user's sleeping posture is in dynamic change, the user's sleeping posture state of the previous frame rate, that is, the background frame, is also a variable. Each time the analysis is performed, the background frame needs to be updated, that is, the previous moment of the current moment is updated to the background frame as the first dynamic variable.

[0072] The second dynamic variable is calculated based on the variable value (such as the change of real-time posture point cloud or sensor data), reflecting the instant change of the user's sleeping posture. Compared with the variables of the background frame, these dynamic variables provide basic data support for subsequent control decisions.

[0073] Next, according to the first dynamic variable, based on the relative relationship, the baseline control condition is matched, that is, according to the first dynamic variable, that is, the state of the background frame, according to the corresponding relationship, the corresponding monitoring condition under the background frame is matched and determined as the baseline control condition.

[0074] Subsequently, a conditional variable is determined based on the second dynamic variable. The second dynamic variable reflects the real-time changes in the current state of the target user. By analyzing this variable, it is possible to determine the immediate fluctuations or changes in the user's sleeping posture, so as to determine whether the monitoring and control conditions need to be adjusted and the amount of adjustment. For example, if the second dynamic variable shows that the user's posture has changed significantly (such as turning over), the monitoring strategy needs to be adjusted according to this change. The conditional variable refers to the monitoring conditions that the system needs to adjust under such immediate changes, such as increasing the frequency of collecting respiratory signals or adjusting the sensitivity of monitoring. Preferably, when the second dynamic variable is small, there is no need to adjust the monitoring conditions, and monitoring can continue to be performed based on the baseline control conditions.

[0075] In the specific implementation process, the monitoring control condition of the current posture is first determined based on the relative relationship, and the difference between the current posture and the baseline control condition is calculated as the condition variable. At present, the sleep monitor should be in the baseline control condition, and the condition variable is adjusted to meet the current monitoring needs.

[0076] Then, the conditional variable, i.e., the width of the monitoring control condition, is adjusted on the basis of the baseline control condition, and the adjusted value is used as the target control condition to adapt to the current state change of the user. This adjustment usually involves the selection of monitoring reference frequency, signal channel or sampling frequency change. The target control condition refers to the final adjustment made by the system based on the immediate change, which can respond to the user's needs and state changes in real time, ensuring the optimal breathing monitoring under different sleeping positions and physiological changes.

[0077] In summary, this embodiment realizes a real-time adjustment process based on sleeping position variables by identifying and analyzing the first dynamic variable and the second dynamic variable. Through this dynamic adjustment, the system can flexibly match the baseline control conditions according to the sleeping position changes of the target user, and adjust to the target control conditions when necessary, thereby ensuring that the respiratory monitor always provides accurate and personalized monitoring services.

[0078] S4: transmitting the respiratory signal back and performing histogram segmentation and counting, determining the respiratory state and performing feedback response control on the respiratory monitor.

[0079] This embodiment involves further determining the user's breathing state by returning and analyzing the histogram of the breathing signal, and performing feedback response control on the breathing monitor according to the analysis result.

[0080] First, the breathing signal is transmitted back and histogram segmentation and counting are performed. The breathing signal is usually collected by a sensor and transmitted back to the control system of the respiratory monitor. The transmitted signals include physical signals reflecting the breathing status, such as the user's chest and abdomen rise and fall, airflow changes, etc. By transmitting these signals back, the system can obtain real-time breathing data for subsequent processing.

[0081] Next, the returned respiratory signal is analyzed using histogram segmentation and counting techniques. A histogram is a statistical graph that divides the amplitude value of a signal into multiple intervals (i.e., "boxes") and counts the signal frequency in each interval. The purpose of histogram segmentation is to identify different stages in the respiratory cycle, such as inspiration, exhalation, or pauses, based on the amplitude changes of the respiratory signal. Histogram counting counts the signal amplitude that appears in each interval to determine the characteristics of the respiratory waveform. In this way, the system can clearly identify different respiratory events.

[0082] Then, based on the signal amplitude and frequency distribution in the histogram, it is identified whether there are abnormal breathing patterns, such as shallow breathing, pauses, or frequent changes. Under normal circumstances, the breathing state should remain stable, and the signal amplitude and frequency distribution should conform to the expected pattern. If the histogram analysis indicates an abnormality, the system will determine whether the breathing is within the normal range based on the preset threshold, and then determine the user's health status. For example, if the signal amplitude is too large or too small, or the frequency is too high or too low, it may mean that the user is experiencing abnormal breathing fluctuations and needs to respond in a timely manner.

[0083] Next, the respiratory monitor is subjected to feedback response control. Once the system determines the user's respiratory state through histogram analysis, it will perform feedback control on the respiratory monitor based on the analysis results. Feedback control means that the system adjusts the monitoring strategy and equipment settings according to the current respiratory state to better adapt to the user's health status. For example, when an abnormal user's respiratory state is detected (such as apnea or shallow breathing), the system will adjust the sensitivity, sampling frequency or alarm threshold of the monitor, and even issue an alarm when necessary. The purpose of feedback response is to ensure that the respiratory monitor can provide timely and accurate monitoring under any circumstances, and that corresponding measures can be taken immediately when abnormalities are detected.

[0084] Furthermore, the above-mentioned histogram segmentation and counting are performed to determine the respiratory state. Step S4 of the present application includes:

[0085] The respiratory signal is transmitted back and filtered to reduce noise to determine the respiratory waveform; the respiratory waveform is identified based on the respiratory cycle and the respiratory events, the signal segments are segmented and counted, and a signal histogram is constructed, wherein the horizontal axis represents the amplitude value of the signal and the vertical axis represents the amplitude value frequency; the respiratory state is evaluated according to the signal histogram.

[0086] This embodiment evaluates the breathing state of the target user by performing steps such as filtering and denoising, waveform recognition, signal segmentation, and signal histogram construction on the returned breathing signal.

[0087] First, the respiratory signal is transmitted back and filtered and denoised to determine the respiratory waveform. In this step, the respiratory signal collected from the sensor and transmitted back to the control system is first preprocessed. Since the original signal may be interfered by noise (such as electromagnetic interference, motion noise, etc.), it is necessary to eliminate these noises through a filtering and denoising algorithm to ensure the accuracy of the signal. Common filtering methods include low-pass filtering, high-pass filtering or band-pass filtering, and the specific selection depends on the frequency range to be extracted. After the noise reduction process is completed, the system obtains a clearer respiratory waveform that accurately reflects the user's breathing process, including the periodic changes of inspiration and exhalation.

[0088] Next, the respiratory waveform is identified based on the determination of the respiratory cycle and the determination of respiratory events. After obtaining a clear respiratory waveform, the periodic characteristics of the respiratory waveform are further analyzed. The respiratory cycle refers to the complete process of the user from inhalation to exhalation and then back to inhalation. In this process, the starting and ending points of each inhalation and exhalation are identified through periodic detection, and then a complete respiratory cycle is determined. The duration of each cycle and its changes can reflect the user's breathing state. For example, a breathing cycle that is too short or too long may mean an abnormal state. In addition, the system can also determine respiratory events based on changes in the amplitude of the waveform, such as short apnea or frequent shallow breathing.

[0089] Then, the signal segments are segmented and counted. Based on the aforementioned identified breathing cycles, the system divides the entire breathing waveform into several signal segments, each of which represents a complete breathing cycle. In each signal segment, the system counts the amplitude value and frequency of the waveform to extract features that help evaluate the breathing state. For example, the system can calculate the maximum amplitude, minimum amplitude, frequency, and periodic stability of each signal segment. Through detailed statistics of each signal segment, the system can fully understand the user's breathing pattern.

[0090] Next, construct a signal histogram. After segmenting and counting each signal segment, the system will construct a signal histogram based on these count data. A histogram is a graphical representation of data distribution, with the horizontal axis representing the amplitude value of the signal and the vertical axis representing the frequency of occurrence within the amplitude value range. By constructing a signal histogram, the system can intuitively understand the distribution of the user's breathing signal. If the histogram shows a normal distribution, it means that the user's breathing state is normal; if the histogram shows an abnormal distribution, such as the frequency is concentrated in a lower or higher amplitude range, it may mean that there is a breathing abnormality.

[0091] Finally, the breathing state is evaluated based on the signal histogram. The system evaluates the user's breathing state based on the constructed histogram and the preset health standards. For example, if the histogram shows that most of the signal amplitudes are concentrated in the lower range, it may indicate that the user's breathing is shallow; if the amplitude is too large or the frequency is too high, it may indicate that the user has shortness of breath or other abnormal problems. In this way, the system can accurately evaluate the user's breathing state and provide timely feedback and adjustments.

[0092] In summary, it is possible to comprehensively analyze the user's respiratory signals and accurately assess their respiratory status. The implementation of this process ensures that the monitor can monitor the user's respiratory status in real time, and issue an alarm or adjust the monitoring strategy when necessary, providing more personalized health management.

[0093] Further, the respiratory monitor is subjected to feedback response control, and step S4 of the present application includes:

[0094] configuring a risk threshold, wherein the risk threshold includes a static threshold and a dynamic threshold; evaluating the respiratory state according to the risk threshold, and the respiratory monitor performing state feedback response control;

[0095] Among them, the state feedback response control includes: if the static threshold exceeds the limit and the dynamic threshold exceeds the limit, a first alarm message is generated; if the static threshold exceeds the limit or the dynamic threshold exceeds the limit, the monitoring control condition is adjusted and a second alarm message is generated, wherein the intensity of the second alarm message is lower than that of the first alarm message.

[0096] This embodiment monitors and adjusts the breathing state of the target user in real time to ensure their health and safety by configuring risk thresholds and executing status feedback response control.

[0097] First, configure the risk threshold, including static threshold and dynamic threshold. In this step, the system sets static threshold and dynamic threshold for the respiratory monitor. Static threshold is usually based on the theoretical standard value of normal human breathing parameters (such as normal breathing frequency, depth, rhythm, etc.), which is suitable for long-term stable monitoring conditions. The dynamic threshold is set according to the changes in real-time monitoring data, which can flexibly respond to fluctuations in the user's breathing pattern and is suitable for detecting abnormal changes in the short term. The combination of static threshold and dynamic threshold ensures that the monitoring system can comprehensively evaluate the respiratory status, whether it is long-term stability or short-term volatility.

[0098] Next, the respiratory state is evaluated according to the risk threshold. After completing the threshold configuration, the system will compare the real-time acquired respiratory signal with the static threshold and dynamic threshold respectively. If the respiratory signal exceeds the set range (such as the respiratory rate is too fast or too slow, the respiratory amplitude is abnormal, etc.), the evaluation mechanism is triggered, and the feedback measures are further determined based on the evaluation results.

[0099] Then, the respiratory monitor performs state feedback response control. According to the evaluation results, the respiratory monitor will take feedback response control measures. This control measure includes generating alarm information or adjusting monitoring conditions to promptly respond to the user's breathing abnormalities.

[0100] Specifically, the state feedback response control includes: if the static threshold exceeds the limit and the dynamic threshold exceeds the limit, a first alarm message is generated. First, when the respiratory signal not only exceeds the range of the static threshold, but also exceeds the limit of the dynamic threshold, it means that the respiratory state is abnormal and the change is large. At this time, the system will generate a first alarm message to inform the user or medical staff that immediate action is needed. This type of alarm message is usually of high intensity and high priority, and is an emergency alarm.

[0101] Secondly, if the static threshold or the dynamic threshold exceeds the limit, in this case, the system believes that although the monitoring data has deviated from the normal range, the degree of deviation is relatively minor, so a second alarm message is generated. The intensity of the second alarm message is lower than that of the first alarm message, which usually indicates that the abnormality is relatively minor. In order to further optimize the monitoring effect, the system will also adjust the monitoring control conditions according to the situation, such as appropriately reducing the monitoring sensitivity or adjusting the monitoring frequency to adapt to the user's current situation.

[0102] By configuring static and dynamic thresholds and combining them with real-time evaluation, the respiratory monitor can flexibly respond to various respiratory states, provide timely feedback on abnormalities, and protect the health of users. At the same time, through the adjustment of alarm information of different intensities and monitoring control conditions, the system can make corresponding treatments according to different abnormalities to ensure the accuracy and adaptability of monitoring.

[0103] The present application provides a respiratory monitor control method combined with sleeping posture analysis, which has the following technical effects:

[0104] 1. The scheme has designed multiple signal channels and multi-level monitoring frequencies, and uses chest and abdominal breathing signal channels, airflow breathing signal channels and auxiliary signal channels. Through multi-channel and multi-frequency signal acquisition, the system can fully capture the multi-dimensional breathing data of the target user and optimize the monitoring strategy according to the characteristics of different signal sources. It realizes accurate monitoring of complex physiological states and improves the reliability and accuracy of data.

[0105] 2. By introducing cascade sensors and combining the inter-frame difference calculation of sleeping posture point cloud data with cascade sensor data, the sleeping posture variables are determined, the control conditions are adaptively adjusted, and accurate sleeping posture monitoring is performed. The system can automatically adjust the monitoring strategy in real time under different sleeping postures to ensure that the personalized needs of each user are responded to, thereby improving the adaptability and intelligence of the system.

[0106] 3. Based on the identification of respiratory cycles and the determination of respiratory events, a signal histogram is constructed. Through signal histogram analysis, changes in breathing patterns can be intuitively displayed, supporting more sophisticated health assessments. Static thresholds and dynamic thresholds are configured to evaluate respiratory status and perform status feedback response control. By setting multi-level risk thresholds and alarm mechanisms, the system can sensitively identify abnormal conditions and provide timely feedback. Alarms of different intensities help distinguish the severity of abnormalities, ensuring that the system can flexibly respond to minor or severe respiratory abnormalities and protect the health of users.

[0107] In summary, this technical solution realizes an accurate and personalized respiratory monitoring system. The combination of technical points improves the stability, accuracy and responsiveness of the system, ensuring real-time and efficient health monitoring services in complex physiological environments.

[0108] Through the above detailed description of a method for controlling a respiratory monitor combined with sleep posture analysis, those skilled in the art can clearly understand the method for controlling a respiratory monitor combined with sleep posture analysis in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0109] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A respiratory monitor control method combined with sleeping posture analysis, characterized in that: The method comprises: A cascade sensor is introduced to determine the sleeping posture variable by collecting the state of the target user and coupling the inter-frame difference of the posture point cloud data and the cascade sensor data, wherein the cascade sensor is used to detect the pressure data of various parts of the human body; Interactive respiratory monitoring records and mines the relative relationship between sleeping posture and monitoring control conditions, and develops an adaptive control module in the control system of the respiratory monitor, wherein the monitoring control conditions are determined by dividing the signal channels of the respiratory monitor and grading the monitoring frequency parameters; Taking the sleeping posture variable as the control target, executing the adjustment decision based on the adaptive control module, triggering the condition signal collection of the target user, and determining the breathing signal; The respiratory signal is fed back and histogram segmentation and counting are performed to determine the respiratory state and perform feedback response control on the respiratory monitor.

2. A respiratory monitor control method combined with sleeping posture analysis as claimed in claim 1, characterized in that: Determining the sleeping posture variable comprises: establishing a communication connection between the cascade sensor and the respiratory monitor; Introducing a synchronization timestamp constraint, the respiratory monitor performs sleeping posture radar detection of the target user, synchronously triggers the sensor data return of the cascade sensor, and determines the real-time posture point cloud and the real-time cascade sensor data; The real-time posture point cloud and the real-time cascade sensor data are coupled with inter-frame differential to determine sleeping posture variables.

3. A respiratory monitor control method combined with sleeping posture analysis as claimed in claim 2, characterized in that: Performing inter-frame differential coupling on the real-time posture point cloud and the real-time cascade sensor data to determine sleeping posture variables includes: Taking the upper posture point cloud as the background frame and the real-time posture point cloud as the state frame, performing inter-frame difference calculation to determine the first posture variable; The upper cascade sensor data is used as the background frame, and the real-time cascade sensor data is used as the state frame, and the frame difference calculation is performed to determine the second cascade sensor variable; The first posture variable and the second cascade sensor variable are coupled based on relative posture to determine the sleeping posture variable.

4. The method for controlling a respiratory monitor combined with sleep posture analysis according to claim 1, characterized in that: The relative relationship between digging out sleeping posture and monitoring control conditions includes: Dividing the signal channels, wherein the signal channels at least include a first signal channel based on chest and abdominal breathing and a second signal channel based on airflow breathing, the signal elements of the first signal channel at least include chest and abdominal fluctuations, and the signal elements of the second signal channel at least include temperature changes, respiratory frequency, and ventilation volume; Divide the monitoring frequency into multiple levels; The monitoring control conditions are determined by the signal channel-multi-level monitoring reference frequency, and the relative relationship between the sleeping posture and the monitoring control conditions is excavated in combination with the respiratory monitoring record.

5. A respiratory monitor control method combined with sleeping posture analysis as claimed in claim 4, characterized in that: The dividing signal channel comprises: Introducing a third signal channel, wherein the third signal channel is an auxiliary signal channel, and the third signal source at least includes an electrocardiogram signal and a breathing sound signal, and is used to collect any one of the third signal sources; The control system is initialized and configured mainly with the first signal channel and the second signal channel and supplemented with the third signal channel, wherein single-channel acquisition, multi-channel same-frequency acquisition, and multi-channel difference-frequency acquisition are acquisition modes.

6. A respiratory monitor control method combined with sleeping posture analysis as claimed in claim 3, characterized in that: Executing a regulation decision based on the adaptive control module includes: Identify the sleeping posture variable, determine a first dynamic variable based on the background frame, and a second dynamic variable based on the variable value; According to the first dynamic variable, matching a baseline control condition based on the relative relationship; determining a conditional variable according to the second dynamic variable; The baseline control condition is adjusted based on the condition variable to determine the target control condition.

7. The method for controlling a respiratory monitor combined with sleep posture analysis according to claim 1, characterized in that: The performing of histogram segmentation and counting to determine the respiratory state includes: The respiratory signal is transmitted back and filtered to reduce noise, so as to determine the respiratory waveform; The respiratory waveform is identified based on the determination of the respiratory cycle and the determination of the respiratory event, the signal segments are segmented and counted, and a signal histogram is constructed, wherein the horizontal axis represents the amplitude value of the signal and the vertical axis represents the amplitude value frequency; Based on the signal histogram, the respiratory state is evaluated.

8. The method for controlling a respiratory monitor combined with sleep posture analysis as claimed in claim 1, characterized in that: Feedback response control is performed on the respiratory monitor, including: Configure risk thresholds, wherein the risk thresholds include static thresholds and dynamic thresholds; The respiratory state is evaluated according to the risk threshold, and the respiratory monitor performs state feedback response control; Among them, the state feedback response control includes: If the static threshold exceeds the limit and the dynamic threshold exceeds the limit, a first alarm message is generated; If the static threshold value exceeds the limit or the dynamic threshold value exceeds the limit, the monitoring control condition is further adjusted and a second alarm information is generated, wherein the intensity of the second alarm information is lower than that of the first alarm information.

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